Electrochemical Detection of Plasma Immunoglobulin as a Biomarker for Alzheimer's Disease

A new insight into the early detection of HER2 protein in breast cancer patients with a focus on electrochemical biosensors approaches: A review

Breast cancer is one of the leading causes of death in women and is a prevalent kind of cancerous growth, representing a substantial risk to women's health. Early detection of breast cancer is essential for effective treatment and improved survival rates. Biomarkers, active substances that signal the existence and advancement of a tumor, play a significant role in the early detection of breast cancer. Hence, accurate identification of biomarkers for tumors is crucial for diagnosing and treating breast cancer. However, the primary diagnostic methods used for the detection of breast cancer require specific equipment, skilled professionals, and specialized analysis, leading to elevated detection expenses. Regarding this obstacle, recent studies emphasize electrochemical biosensors as more advanced and sensitive detection tools compared to traditional methods. Electrochemical biosensors are employed to identify biomarkers that act as unique indicators for the onset, recurrence, and monitoring of therapeutic interventions for breast cancer. This study aims to provide a summary of the electrochemical biosensors that have been employed for the detection of breast cancer at an early stage over the past decade. Initially, the text provides concise information about breast cancer and tumor biomarkers. Subsequently, an in-depth analysis is conducted to systematically review the progress of electrochemical biosensors developed for the stable, specific, and sensitive identification of biomarkers associated with breast cancer. Particular emphasis was given to crucial clinical biomarkers, specifically the human epidermal growth factor receptor-2 (HER2). The analysis then explores the limitations and challenges inherent in the design of effective biosensors for diagnosing and treating breast cancer. Ultimately, we provided an overview of future research directions and concluded by outlining the advantages of electrochemical biosensor approaches.

Electrochemical detection of bacterial endotoxin lipopolysaccharide (LPS) on gold electrode modified with DAL-PEG-DK5-PEG-OH - Antimicrobial peptide conjugate

This work describes fabrication of gold electrodes modified with peptide conjugate DAL-PEG-DK5-PEG-OH that enables ultra-sensitive detection of lipopolysaccharide (LPS) isolated from the reference strain of Escherichia coli O26:B6. The initial step of the established procedure implies immobilization of the fully protected DAL-PEG-DK5-PEG-OH peptide on the surface of the gold electrode previously modified by cysteamine. Then side chain- and Fmoc-deprotection was performed in situ on the electrode surface, followed by its incubation in 1 % of BSA solution to block non-specific bindings sites before LPS detection. The efficiency of the modification was confirmed by X-ray Photoelectron Spectroscopy (XPS) measurements. Additionally, the cyclic voltammetry (CV) and electrochemical impendance spectroscopy (EIS) were employed to monitor the effectiveness of each step of the modification. The obtained results confirmed that the presence of the surface-attached covalently bound peptide DAL-PEG-DK5-PEG-OH enables LPS detection by means of CV technique within the range from 5 × 10-13 to 5 × 10-4 g/mL in PBS solution. The established limit of detection (LOD) for EIS measurements was 4.93 × 10-21 g/mL with wide linear detection range from 5 × 10-21 to 5 × 10-14 g/mL in PBS solution. Furthermore, we confirmed the ability of the electrode to detect LPS in a complex biological samples, like mouse urine and human serum. The effectiveness of the electrodes in identifying LPS in both urine and serum matrices was confirmed for samples containing LPS at both 2.5 × 10-15 g/mL and 2.5 × 10-9 g/mL.

Electrochemical detection of myeloperoxidase (MPO) in blood plasma with surface-modified electroless nickel immersion gold (ENIG) printed circuit board (PCB) electrodes

Printed circuit board (PCB) based biosensors have often utilized hard gold electroplating, that nullifies the cost advantages of this technology as compared to screen printed electrodes. Electroless nickel immersion gold (ENIG) is a popular gold deposition process widely used in PCB manufacturing, but vulnerable to pinhole defects and large surface roughness, which compromises biosensor performance. In this work, we present a method to address these challenges through electrodeposition of methylene blue (MB) to cover surface defects and improve electroactivity of ENIG PCB electrodes. We also demonstrate a process to realize in situ synthesis of gold nanoparticles (AuNPs) using acid-functionalized multi-walled carbon nanotubes (MWCNTs) as scaffold, that are used to immobilize antibody for the target molecule (myeloperoxidase: MPO, early warning biomarker for cardiovascular diseases) through a modified cysteamine/gluteraldehyde based process. The processing steps on the electrode surface are developed in a manner that do not compromise the integrity of the electrode, resulting in repeatable and reliable performance of the sensors. Further, we demonstrate a cost-effective microfluidic packaging process to integrate a capillary pump driven microfluidic channel on the PCB electrode for seamless introduction of samples for testing. We demonstrate the ability of the sensor to distinguish clinically abnormal concentrations of MPO from normal concentrations through extensive characterization using spiked serum and blood plasma samples, with a limit of detection of 15.79 ng/mL.

Formation of a Conducting Polymer by Different Electrochemical Techniques and Their Effect on Obtaining an Immunosensor for Immunoglobulin G

In this work, a conducting polymer (CP) was obtained through three electrochemical procedures to study its effect on the development of an electrochemical immunosensor for the detection of immunoglobulin G (IgG-Ag) by square wave voltammetry (SWV). The glassy carbon electrode modified with poly indol-6-carboxylic acid (6-PICA) applied the cyclic voltammetry technique presented a more homogeneous size distribution of nanowires with greater adherence allowing the direct immobilization of the antibodies (IgG-Ab) to detect the biomarker IgG-Ag. Additionally, 6-PICA presents the most stable and reproducible electrochemical response used as an analytical signal for developing a label-free electrochemical immunosensor. The different steps in obtaining the electrochemical immunosensor were characterized by FESEM, FTIR, cyclic voltammetry, electrochemical impedance spectroscopy, and SWV. Optimal conditions to improve performance, stability, and reproducibility in the immunosensing platform were achieved. The prepared immunosensor has a linear detection range of 2.0-16.0 ng·mL^-1 with a low detection limit of 0.8 ng·mL^-1. The immunosensing platform performance depends on the orientation of the IgG-Ab, favoring the formation of the immuno-complex with an affinity constant (Ka) of 4.32 × 10⁹ M^-1, which has great potential to be used as point of care testing (POCT) device for the rapid detection of biomarkers.

Hydrogenated Boron Phosphide THz-Metamaterial-Based Biosensor for Diagnosing COVID-19: A DFT Coupled FEM Study

Recent reports focus on the hydrogenation engineering of monolayer boron phosphide and simultaneously explore its promising applications in nanoelectronics. Coupling density functional theory and finite element method, we investigate the bowtie triangle ring microstructure composed of boron phosphide with hydrogenation based on structural and performance analysis. We determine the carrier mobility of hydrogenated boron phosphide, reveal the effect of structural and material parameters on resonance frequencies, and discuss the variation of the electric field at the two tips. The results suggest that the mobilities of electrons for hydrogenated BP monolayer in the armchair and zigzag directions are 0.51 and 94.4 cm2·V-1·s-1, whereas for holes, the values are 136.8 and 175.15 cm2·V-1·s-1. Meanwhile, the transmission spectra of the bowtie triangle ring microstructure can be controlled by adjusting the length of the bowtie triangle ring microstructure and carrier density of hydrogenated BP. With the increasing length, the transmission spectrum has a red-shift and the electric field at the tips of equilateral triangle rings is significantly weakened. Furthermore, the theoretical sensitivity of the BTR structure reaches 100 GHz/RIU, which is sufficient to determine healthy and COVID-19-infected individuals. Our findings may open up new avenues for promising applications in the rapid diagnosis of COVID-19.

Use of Cysteamine and Glutaraldehyde Chemicals for Robust Functionalization of Substrates with Protein Biomarkers—An Overview on the Construction of Biosensors with Different Transductions

Currently, several biosensors are reported to confirm the absence/presence of an abnormal level of specific human biomarkers in research laboratories. Unfortunately, public marketing and/or pharmacy accessibility are not yet possible for many bodily fluid biomarkers. The questions are numerous, starting from the preparation of the substrates, the wet/dry form of recognizing the (bio)ligands, the exposure time, and the choice of the running buffers. In this context, for the first time, the present overview summarizes the pre-functionalization of standard and nanostructured solid/flexible supports with cysteamine (Cys) and glutaraldehyde (GA) chemicals for robust protein immobilization and detection of biomarkers in body fluids (serum, saliva, and urine) using three transductions: piezoelectrical, electrochemical, and optical, respectively. Thus, the reader can easily access and compare step-by-step conjugate protocols published over the past 10 years. In conclusion, Cys/GA chemistry seems widely used for electrochemical sensing applications with different types of recorded signals, either current, potential, or impedance. On the other hand, piezoelectric detection via quartz crystal microbalance (QCM) and optical detection by surface plasmon resonance (LSPR)/surface-enhanced Raman spectroscopy (SERS) are ultrasensitive platforms and very good candidates for the miniaturization of medical devices in the near future.

A Disposable Saliva Electrochemical MIP-Based Biosensor for Detection of the Stress Biomarker α-Amylase in Point-of-Care Applications

The design and synthesis of artificial receptors based on molecular imprinting (MI) technology for the development of a new MIP-based biosensor for detection of the stress biomarker α-amylase in human saliva in point-of-care (PoC) applications is described in this work. The portable electrochemical devices for monitoring α-amylase consists of cost-effective and disposable gold screen-printed electrodes (AuSPEs). To build the electrochemical device, the template biomolecule was firstly immobilized directly over the working area of the gold chip previously activated with a self-assembled monolayer (SAM) of cysteamine (CA). Then, pyrrole (Py) monomer was selected as building block of a polymeric network prepared by CV electropolymerization. After the electropolymerization process, the enzyme was removed from the polymer film in order to build the specific recognition sites for the target enzyme. The MIP biosensor showed a very wide linear concentration range (between 3.0 × 10−4 to 0.60 mg mL−1 in buffer solution and between 3.0 × 10−4 to 3.0 × 10−2 mg mL−1 in human saliva) and low detection levels were achieved (LOD < 3.0 × 10−4 mg mL−1) using square wave voltammetry (SWV) as the electroanalytical technique.

Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Background:

A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

Method:

The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

Results:

The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Conclusion:

Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Ultrasensitive electrochemical determination of the cancer biomarker protein sPD-L1 based on a BMS-8-modified gold electrode

This work describes the modification of a gold electrode with the BMS-8 compound that interacts with the Programmed Death-Ligand 1 (PD-L1), an immune checkpoint protein. The results show that we can confirm the presence of the sPD-L1 in the concentration range of 10^-18 to 10^-8 M using electrochemical impedance spectroscopy (EIS) with a limit of detection (LOD) of 1.87 × 10^-14 M for PD-L1 (S/N = 3.3) and at a concentration of 10^-14 M via cyclic voltammetry (CV). Additionally, high-resolution X-ray photoelectron spectroscopy (XPS), contact angle, and surface free energy measurements were applied to confirm the functionalization of the electrode. We investigated the selectivity of the electrode for other proteins: Programmed Death-1 (PD-1), cluster of differentiation 160 (CD160), and B- and T-lymphocyte attenuator (BTLA) at concentrations of 10^-8 M. Differentiation between PD-L1 and PD-1 was achieved based on the analysis of the capacitance effect frequency dispersion at the surface of the modified Au electrode with BMS-8 after incubation at various concentrations of PD-L1 and PD-1 proteins in the range of 10^-18 to 10^-8 M. Significant differences were observed in the heterogeneity of PD-L1 and PD-1. The results of the quasi-capacitance studies demonstrate that BMS-8 strongly and specifically interacts with the PD-L1 protein.

Nanobiosensors for Non-Amyloidbeta-Tau Biomarkers as Advanced Reporters of Alzheimer's Disease

Emerging nanomaterials providing benefits in sensitivity, specificity and cost-effectiveness are being widely investigated for biosensors in the application of Alzheimer's disease (AD) diagnosis. Core biomarkers amyloid-beta (Aβ) and Tau have been considered as key neuropathological hallmarks of AD. However, they did not sufficiently reflect clinical severity and therapeutic response, proving the difficulty of the Aβ- and Tau-targeting therapies in clinical trials. In recent years, there has still been a shortage of sensors for non-Aβ-Tau pathophysiological biomarkers that serve as advanced reporters for the early diagnosis of AD, predict AD progression, and monitor the treatment response. Nanomaterial-based sensors measuring multiple non-Aβ-Tau biomarkers could improve the capacity of AD progression characterization and supervised treatment, facilitating the comprehensive management of AD. This is the first review to principally represent current nanobiosensors for non-Aβ-Tau biomarker and that strategically deliberates future perspectives on the merit of non-Aβ-Tau biomarkers, in combination with Aβ and Tau, for the accurate diagnosis and prognosis of AD.

Electrochemical Study of Dimensional Specific Carbon Nanomaterials Modified Glassy Carbon Electrode for Highly Sensitive Label-free Detection of Immunoglobulin A

Background:

Immunoglobulin A (IgA) accounts for 15% of total protein production per day and plays a crucial role in the first-line immune defence. Recently, IgA has been established as a vital clinical biomarker for nephropathy, allergic asthma, celiac disease (CD), pneumonia, and asthma as well as some neurological disorders. In this work, we have studied several carbon nanomaterials (CNMs) having different dimensions (D): carbon nano-onions (CNOs) - 0D, single-walled carbon nanotubes (SWCNTs) - 1D, and graphene nanoplatelets (GNPs) - 2D, on glassy carbon electrode (GCE) to identify which CNMs (CNOs/SWCNTs/GNPs) work best to fabricate IgA based electrochemical immunosensor.

Methods:

Different CNMs (CNOs, SWCNTs, GNPs) were tested for high electric current on GCE using square wave voltammetry (SWV), and among them, GNPs modified GCE platform (GNPs/GCE) showcased the highest electric current. Therefore, GNPs/GCE was utilized for the development of highly sensitive label-free electrochemical immunosensor for the detection of Immunoglobulin A using SWV.

Results:

Despite the simple fabrication strategies employed, the fabricated sensor demonstrated a low limit of detection of 50 fg mL-1 with an extensive linear range of detection from 50 fg mL-1 to 0.1 μg mL-1.

Conclusion:

Fabricated immunosensor represented high stability, repeatability, specificity and resistance to most common interferences as well as great potential to analyse the real sample.

Recent advances in aptamer-based sensors for breast cancer diagnosis: special cases for nanomaterial-based VEGF, HER2, and MUC1 aptasensors

Cancer is one of the most common and important diseases with a high mortality rate. Breast cancer is among the three most common types of cancer in women, and the mortality rate has reached 0.024% in some countries. For early-stage preclinical diagnosis of breast cancer, sensitive and reliable tools are needed. Today, there are many types of biomarkers that have been identified for cancer diagnosis. A wide variety of detection strategies have also been developed for the detection of these biomarkers from serum or other body fluids at physiological concentrations. Aptamers are single-stranded DNA or RNA oligonucleotides and promising in the production of more sensitive and reliable biosensor platforms in combination with a wide range of nanomaterials. Conformational changes triggered by the target analyte have been successfully applied in fluorometric, colorimetric, plasmonic, and electrochemical-based detection strategies. This review article presents aptasensor approaches used in the detection of vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (HER2), and mucin-1 glycoprotein (MUC1) biomarkers, which are frequently studied in the diagnosis of breast cancer. The focus of this review article is on developments of the last decade for detecting these biomarkers using various sensitivity enhancement techniques and nanomaterials.

Detection Strategies of Zearalenone for Food Safety: A Review

Zearalenone (ZEN) is a toxic compound produced by the metabolism of fungi (genus Fusarium) that threaten the food and agricultural industry belonging to the in foods and feeds. ZEN has toxic effects on human and animal health due to its mutagenicity, teratogenicity, carcinogenicity, nephrotoxicity, immunotoxicity, and genotoxicity. To ensure food safety, rapid, precise, and reliable analytical methods can be developed for the detection of toxins such as ZEN. Different selective molecular diagnostic elements are used in conjunction with different detection strategies to achieve this goal. In this review, the use of electrochemical, colorimetric, fluorometric, refractometric as well as other strategies were discussed for ZEN detection. The success of the sensors in analytical performance depends on the development of receptors with increased affinity to the target. This requirement has been met with different immunoassays, aptamer-assays, and molecular imprinting techniques. The immobilization techniques and analysis strategies developed with the combination of nanomaterials provided high precision, reliability, and convenience in ZEN detection, in which electrochemical strategies perform the best.

Paper-Based Detection Device for Alzheimer's Disease-Detecting β-amyloid Peptides (1-42) in Human Plasma

The diagnosis of Alzheimer's disease (AD) is frequently missed or delayed in clinical practice. To remedy this situation, we developed a screening, paper-based (P-ELISA) platform to detect β-amyloid peptide 1-42 (Aβ42) and provide rapid results using a small volume, easily accessible plasma sample instead of cerebrospinal fluid. The protocol outlined herein only requires 3 μL of sample per well and a short operating time (i.e., only 90 min). The detection limit of Aβ42 is 63.04 pg/mL in a buffer system. This P-ELISA-based approach can be used for early, preclinical stage AD screening, including screening for amnestic mild cognitive impairment (MCI) due to AD. It may also be used for treatment and stage monitoring purposes. The implementation of this approach may provide tremendous impact for an afflicted population and may well prompt additional and expanded efforts in both academic and commercial communities.

Emphasis Learning, Features Repetition in Width Instead of Length to Improve Classification Performance: Case Study-Alzheimer's Disease Diagnosis

In the past decade, many studies have been conducted to advance computer-aided systems for Alzheimer's disease (AD) diagnosis. Most of them have recently developed systems concentrated on extracting and combining features from MRI, PET, and CSF. For the most part, they have obtained very high performance. However, improving the performance of a classification problem is complicated, specifically when the model's accuracy or other performance measurements are higher than 90%. In this study, a novel methodology is proposed to address this problem, specifically in Alzheimer's disease diagnosis classification. This methodology is the first of its kind in the literature, based on the notion of replication on the feature space instead of the traditional sample space. Briefly, the main steps of the proposed method include extracting, embedding, and exploring the best subset of features. For feature extraction, we adopt VBM-SPM; for embedding features, a concatenation strategy is used on the features to ultimately create one feature vector for each subject. Principal component analysis is applied to extract new features, forming a low-dimensional compact space. A novel process is applied by replicating selected components, assessing the classification model, and repeating the replication until performance divergence or convergence. The proposed method aims to explore most significant features and highest-preforming model at the same time, to classify normal subjects from AD and mild cognitive impairment (MCI) patients. In each epoch, a small subset of candidate features is assessed by support vector machine (SVM) classifier. This repeating procedure is continued until the highest performance is achieved. Experimental results reveal the highest performance reported in the literature for this specific classification problem. We obtained a model with accuracies of 98.81%, 81.61%, and 81.40% for AD vs. normal control (NC), MCI vs. NC, and AD vs. MCI classification, respectively.